Devising a procedure for the brightness alignment of astronomical frames background by a high frequency filtration to improve accuracy of the brightness estimation of objects

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.301327

Keywords:

brightness equalization, high-pass filtering, ideal filter, Butterworth filter, Gaussian filter

Abstract

The object of this study is the background substrate of astronomical frames. To detect and compare the image of an object in a frame with its real image from astronomical catalogs, it is necessary to uniformly distribute the brightness of the background image substrate. Most often, the background alignment of astronomical frames is performed using the hardware calibration method applying the construction of service frames. However, it does not make it possible to eliminate the background from temporary stray light. Therefore, to solve this problem, a procedure has been proposed for brightness alignment of the background frame using high-pass filtering.

For high-pass filtering of images, three high-pass filters were considered – an ideal filter, a Butterworth filter, and a Gaussian filter. To remove coarse-grained image components from the image, a high-pass filter was used, which attenuates low-frequency harmonics of the image spectrum while simultaneously passing high-frequency harmonics.

Applying the devised procedure for brightness alignment of the background substrate of the frame has made it possible to increase the signal-to-noise ratio and reduce the dynamic range of the background substrate of the image. The study showed that when assessing brightness and identifying frames, the fitting provides better accuracy of reference to the starry sky. Also, the standard deviation of frame identification errors in this case is 5–7 times less than without using the devised procedure.

The devised procedure for brightness alignment of the background frame substrate was tested in practice within the framework of the CoLiTec project. It was implemented at the stage of intra-frame processing in the Lemur software for automated detection of new objects and tracking of known objects

Author Biographies

Vladimir Vlasenko, National Space Facilities Control and Test Center

PhD

Space Research and Communications Center

Sergii Khlamov, Kharkiv National University of Radio Electronics

PhD, Assistant

Department of Media Systems and Technologies

Vadym Savanevych, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

Department of Systems Engineering

References

  1. Wheeler, L., Dotson, J., Aftosmis, M., Coates, A., Chomette, G., Mathias, D. (2024). Risk assessment for asteroid impact threat scenarios. Acta Astronautica, 216, 468–487. https://doi.org/10.1016/j.actaastro.2023.12.049
  2. Khlamov, S. V., Savanevych, V. E., Briukhovetskyi, O. B., Pohorelov, A. V. (2016). CoLiTec software - detection of the near-zero apparent motion. Proceedings of the International Astronomical Union, 12 (S325), 349–352. https://doi.org/10.1017/s1743921316012539
  3. Savanevych, V. E., Khlamov, S. V., Akhmetov, V. S., Briukhovetskyi, A. B., Vlasenko, V. P., Dikov, E. N. et al. (2022). CoLiTecVS software for the automated reduction of photometric observations in CCD-frames. Astronomy and Computing, 40, 100605. https://doi.org/10.1016/j.ascom.2022.100605
  4. Hu, Z., Bodyanskiy, Y. V., Tyshchenko, O. K., Tkachov, V. M. (2017). Fuzzy Clustering Data Arrays with Omitted Observations. International Journal of Intelligent Systems and Applications, 9 (6), 24–32. https://doi.org/10.5815/ijisa.2017.06.03
  5. Vavilova, I., Pakuliak, L., Babyk, I., Elyiv, A., Dobrycheva, D., Melnyk, O. (2020). Surveys, Catalogues, Databases, and Archives of Astronomical Data. Knowledge Discovery in Big Data from Astronomy and Earth Observation, 57–102. https://doi.org/10.1016/b978-0-12-819154-5.00015-1
  6. Zhang, Y., Zhao, Y., Cui, C. (2002). Data mining and knowledge discovery in database of astronomy. Progress in Astronomy, 20 (4), 312–323.
  7. Chalyi, S., Levykin, I., Biziuk, A., Vovk, A., Bogatov, I. (2020). Development of the technology for changing the sequence of access to shared resources of business processes for process management support. Eastern-European Journal of Enterprise Technologies, 2 (3 (104)), 22–29. https://doi.org/10.15587/1729-4061.2020.198527
  8. Khlamov, S., Savanevych, V., Tabakova, I., Trunova, T. (2022). The astronomical object recognition and its near-zero motion detection in series of images by in situ modeling. 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). https://doi.org/10.1109/iwssip55020.2022.9854475
  9. Troianskyi, V., Kankiewicz, P., Oszkiewicz, D. (2023). Dynamical evolution of basaltic asteroids outside the Vesta family in the inner main belt. Astronomy & Astrophysics, 672, A97. https://doi.org/10.1051/0004-6361/202245678
  10. Akhmetov, V., Khlamov, S., Savanevych, V., Dikov, E. (2019). Cloud Computing Analysis of Indian ASAT Test on March 27, 2019. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). https://doi.org/10.1109/picst47496.2019.9061243
  11. Oszkiewicz, D., Troianskyi, V., Galád, A., Hanuš, J., Ďurech, J., Wilawer, E. et al. (2023). Spins and shapes of basaltic asteroids and the missing mantle problem. Icarus, 397, 115520. https://doi.org/10.1016/j.icarus.2023.115520
  12. Savanevych, V., Khlamov, S., Briukhovetskyi, O., Trunova, T., Tabakova, I. (2023). Mathematical Methods for an Accurate Navigation of the Robotic Telescopes. Mathematics, 11 (10), 2246. https://doi.org/10.3390/math11102246
  13. Bellanger, M. (2024). Digital Signal Processing. John Wiley & Sons. https://doi.org/10.1002/9781394182695
  14. Adam, G. K., Kontaxis, P. A., Doulos, L. T., Madias, E.-N. D., Bouroussis, C. A., Topalis, F. V. (2019). Embedded Microcontroller with a CCD Camera as a Digital Lighting Control System. Electronics, 8 (1), 33. https://doi.org/10.3390/electronics8010033
  15. Savanevych, V., Khlamov, S., Vlasenko, V., Deineko, Z., Briukhovetskyi, O., Tabakova, I., Trunova, T. (2022). Formation of a typical form of an object image in a series of digital frames. Eastern-European Journal of Enterprise Technologies, 6 (2 (120)), 51–59. https://doi.org/10.15587/1729-4061.2022.266988
  16. Klette, R. (2014). Concise Computer Vision. An Introduction into Theory and Algorithms. Springer London, 429. https://doi.org/10.1007/978-1-4471-6320-6
  17. Khlamov, S., Tabakova, I., Trunova, T. (2022). Recognition of the astronomical images using the Sobel filter. 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). https://doi.org/10.1109/iwssip55020.2022.9854425
  18. Dhanalakshmi, R., Bhavani, N. P. G., Raju, S. S., Shaker Reddy, P. C., Mavaluru, D., Singh, D. P., Batu, A. (2022). Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter. Computational Intelligence and Neuroscience, 2022, 1–8. https://doi.org/10.1155/2022/4340897
  19. Savanevych, V., Akhmetov, V., Khlamov, S., Dikov, E., Briukhovetskyi, A., Vlasenko, V. et al. (2019). Selection of the Reference Stars for Astrometric Reduction of CCD-Frames. Advances in Intelligent Systems and Computing, 881–895. https://doi.org/10.1007/978-3-030-33695-0_57
  20. Lösler, M., Eschelbach, C., Riepl, S. (2018). A modified approach for automated reference point determination of SLR and VLBI telescopes. Tm - Technisches Messen, 85 (10), 616–626. https://doi.org/10.1515/teme-2018-0053
  21. Shan, W., Yi, Y., Qiu, J., Yin, A. (2019). Robust Median Filtering Forensics Using Image Deblocking and Filtered Residual Fusion. IEEE Access, 7, 17174–17183. https://doi.org/10.1109/access.2019.2894981
  22. Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., Terzopoulos, D. (2021). Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3059968
  23. Dadkhah, M., Lyashenko, V. V., Deineko, Z. V., Shamshirband, S., Jazi, M. D. (2019). Methodology of wavelet analysis in research of dynamics of phishing attacks. International Journal of Advanced Intelligence Paradigms, 12 (3/4), 220. https://doi.org/10.1504/ijaip.2019.098561
  24. Kirichenko, L., Saif, A., Radivilova, T. (2020). Generalized Approach to Analysis of Multifractal Properties from Short Time Series. International Journal of Advanced Computer Science and Applications, 11 (5). https://doi.org/10.14569/ijacsa.2020.0110527
  25. Hampson, K. M., Gooding, D., Cole, R., Booth, M. J. (2019). High precision automated alignment procedure for two-mirror telescopes. Applied Optics, 58 (27), 7388. https://doi.org/10.1364/ao.58.007388
  26. Kudzej, I., Savanevych, V. E., Briukhovetskyi, O. B., Khlamov, S. V., Pohorelov, A. V., Vlasenko, V. P. et al. (2019). CoLiTecVS – A new tool for the automated reduction of photometric observations. Astronomische Nachrichten, 340 (1-3), 68–70. https://doi.org/10.1002/asna.201913562
  27. Khlamov, S., Vlasenko, V., Savanevych, V., Briukhovetskyi, O., Trunova, T., Chelombitko, V., Tabakova, I. (2022). Development of computational method for matched filtration with analytical profile of the blurred digital image. Eastern-European Journal of Enterprise Technologies, 5 (4 (119)), 24–32. https://doi.org/10.15587/1729-4061.2022.265309
  28. Khlamov, S., Savanevych, V., Vlasenko, V., Briukhovetskyi, O., Trunova, T., Levykin, I. et al. (2023). Development of the matched filtration of a blurred digital image using its typical form. Eastern-European Journal of Enterprise Technologies, 1(9 (121)), 62–71. https://doi.org/10.15587/1729-4061.2023.273674
  29. Burger, W., Burge, M. J. (2022). Digital Image Processing. An Algorithmic Introduction. Springer Cham, 945. https://doi.org/10.1007/978-3-031-05744-1
  30. Bodyanskiy, Y., Popov, S., Brodetskyi, F., Chala, O. (2022). Adaptive Least-Squares Support Vector Machine and its Combined Learning-Selflearning in Image Recognition Task. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT). https://doi.org/10.1109/csit56902.2022.10000518
  31. Troianskyi, V., Kashuba, V., Bazyey, O., Okhotko, H., Savanevych, V., Khlamov, S., Briukhovetskyi, A. (2023). First reported observation of asteroids 2017 AB8, 2017 QX33, and 2017 RV12. Contributions of the Astronomical Observatory Skalnaté Pleso, 53 (2). https://doi.org/10.31577/caosp.2023.53.2.5
  32. Li, T., DePoy, D. L., Marshall, J. L., Nagasawa, D. Q., Carona, D. W., Boada, S. (2014). Monitoring the atmospheric throughput at Cerro Tololo Inter-American Observatory with aTmCam. Ground-Based and Airborne Instrumentation for Astronomy V. https://doi.org/10.1117/12.2055167
  33. Luo, X., Gu, S., Xiang, Y., Wang, X., Yeung, B., Ng, E. et al. (2022). Active longitudes and starspot evolution of the young rapidly rotating star USNO-B1.0 1388−0463685 discovered in the Yunnan–Hong Kong survey. Monthly Notices of the Royal Astronomical Society, 514 (1), 1511–1521. https://doi.org/10.1093/mnras/stac1406
  34. Lemur software. CoLiTec. Available at: https://colitec.space/
  35. Khlamov, S., Savanevych, V., Briukhovetskyi, O., Tabakova, I., Trunova, T. (2022). Data Mining of the Astronomical Images by the CoLiTec Software. CEUR Workshop Proceedings, 3171, 1043–1055. Available at: https://ceur-ws.org/Vol-3171/paper75.pdf
  36. Dougherty, E. R. (2020). Digital Image Processing Methods. CRC Press, 504. https://doi.org/10.1201/9781003067054
  37. Gonzalez, R., Woods, R. (2018). Digital image processing. Pearson. Available at: https://dl.icdst.org/pdfs/files4/01c56e081202b62bd7d3b4f8545775fb.pdf
  38. Shvedun, V. O., Khlamov, S. V. (2016). Statistical modeling for determination of perspective number of advertising legislation violations. Actual Problems of Economics, 184 (10), 389–396.
  39. Perova, I., Brazhnykova, Y., Miroshnychenko, N., Bodyanskiy, Y. (2020). Information Technology for Medical Data Stream Mining. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). https://doi.org/10.1109/tcset49122.2020.235399
  40. Steger, C., Ulrich, M., Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons, 516.
  41. Khlamov, S., Tabakova, I., Trunova, T., Deineko, Z. (2022). Machine Vision for Astronomical Images Using the Canny Edge Detector. CEUR Workshop Proceedings, 3384, 1–10.
  42. Ruban, I., Martovytskyi, V., Lukova-Chuiko, N. (2016). Designing a monitoring model for cluster super–computers. Eastern-European Journal of Enterprise Technologies, 6 (2 (84)), 32–37. https://doi.org/10.15587/1729-4061.2016.85433
  43. Buslov, P., Shvedun, V., Streltsov, V. (2018). Modern Tendencies of Data Protection in the Corporate Systems of Information Consolidation. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). https://doi.org/10.1109/infocommst.2018.8632089
  44. Cavuoti, S., Brescia, M., Longo, G. (2012). Data mining and knowledge discovery resources for astronomy in the web 2.0 age. Software and Cyberinfrastructure for Astronomy II. https://doi.org/10.1117/12.925321
  45. Рetrychenko, A., Levykin, I., Iuriev, I. (2021). Improving a method for selecting information technology services. Eastern-European Journal of Enterprise Technologies, 2 (2 (110)), 32–43. https://doi.org/10.15587/1729-4061.2021.229983
  46. Grebennik, I., Chorna, O., Urniaieva, I. (2022). Distribution of Permutations with Different Cyclic Structure in Mathematical Models of Transportation Problems. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT). https://doi.org/10.1109/acit54803.2022.9913183
  47. Baranova, V., Zeleniy, O., Deineko, Z., Bielcheva, G., Lyashenko, V. (2019). Wavelet Coherence as a Tool for Studying of Economic Dynamics in Infocommunication Systems. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). https://doi.org/10.1109/picst47496.2019.9061301
  48. Dombrovska, S., Shvedun, V., Streltsov, V., Husarov, K. (2018). The prospects of integration of the advertising market of Ukraine into the global advertising business. Problems and Perspectives in Management, 16 (2), 321–330. https://doi.org/10.21511/ppm.16(2).2018.29
Devising a procedure for the brightness alignment of astronomical frames background by a high frequency filtration to improve accuracy of the brightness estimation of objects

Downloads

Published

2024-04-30

How to Cite

Vlasenko, V., Khlamov, S., & Savanevych, V. (2024). Devising a procedure for the brightness alignment of astronomical frames background by a high frequency filtration to improve accuracy of the brightness estimation of objects. Eastern-European Journal of Enterprise Technologies, 2(2 (128), 31–38. https://doi.org/10.15587/1729-4061.2024.301327